Sentiment classification using automatically extracted subgraph features
CAAGET '10: Proceedings of the NAACL HLT 2010 Workshop on Computational Approaches to Analysis and Generation of Emotion in Text(2010)
摘要
In this work, we propose a novel representation of text based on patterns derived from linguistic annotation graphs. We use a subgraph mining algorithm to automatically derive features as frequent subgraphs from the annotation graph. This process generates a very large number of features, many of which are highly correlated. We propose a genetic programming based approach to feature construction which creates a fixed number of strong classification predictors from these subgraphs. We evaluate the benefit gained from evolved structured features, when used in addition to the bag-of-words features, for a sentiment classification task.
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关键词
annotation graph,fixed number,frequent subgraphs,large number,linguistic annotation graph,sentiment classification task,strong classification predictor,bag-of-words feature,derive feature,genetic programming,subgraph feature
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